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AI agents often work well in demos but struggle in production when crashes, restarts, and long-running tasks cause them to lose state and execution context. The challenges facing AI agents today mirror those encountered during the rise of distributed microservices: coordinating long-running processes, handling failures, preserving state, managing retries, and maintaining visibility across complex workflows.